Flexure-FET-Based Receiver With Competitive Binding for Interference Mitigation in Molecular Communication

Abstract

Molecular communication (MC), a biologically-inspired technology, enables applications in nanonetworks and the Internet of Everything (IoE), offering unprecedented potential in intra-body systems for applications such as drug delivery, health monitoring, and disease detection. In this paper, we extend our previous research on the Flexure-FET MC receiver by integrating a competitive binding model to enhance its performance in high-interference environments, where multiple species coexist in the reception space. While previous studies have primarily focused on ligand concentration estimation and detection methods, they have not fully addressed the competition between molecular species for receptor binding sites. The competitive binding framework introduced here plays a pivotal role in multitarget environments, closely reflecting real-world biological scenarios. By accounting for the competition between species for receptor binding sites, this framework enhances the understanding of MC dynamics in complex environments. Leveraging this framework enables the fine-tuning of receptor responses, optimizing Flexure-FET MC receiver performance through adjustments in ligand concentrations and receptor affinities. Through comprehensive performance analysis, we demonstrate that integrating competitive binding underscores the importance of accounting for interference in MC systems to improve reliability and accuracy. While the signal-to-noise ratio (SNR) and symbol error probability (SEP) are impacted by factors such as interferer concentration and receptor dynamics, the framework emphasizes the need to manage these interference factors to optimize the system’s overall performance. The results show that accounting for interference—through competitive binding—provides a more realistic understanding of system behavior and enables the fine-tuning of the receiver’s response, ultimately ensuring more reliable and accurate detection in environments where multiple species are present.